ANNA UNIVERSITY, CHENNAI AFFILIATED INSTITUTIONS...
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1 ANNA UNIVERSITY, CHENNAI AFFILIATED INSTITUTIONS REGULATIONS - 2013 M.E. DIGITAL SIGNAL PROCESSING CURRICULUM I TO IV SEMESTERS (FULL TIME) SEMESTER I SL. NO COURSE CODE COURSE TITLE L T P C THEORY 1. MA7159 Applied Mathematics for Signal Processing Engineers 3 1 0 4 2. DS7101 DSP Processor Architecture and Programming 3 0 0 3 3. AP7101 Advanced Digital Signal Processing 3 1 0 4 4. DS7102 Video Compression Techniques 3 0 0 3 5. CU7102 Advanced Digital Communication Techniques 3 0 0 3 6. Elective - I 3 0 0 3 PRACTICAL 7. DS7111 Digital Signal Processing Laboratory I 0 0 3 2 TOTAL 18 2 3 22 SEMESTER II SL. NO COURSE CODE COURSE TITLE L T P C THEORY 1. DS7201 Advanced Digital Image Processing 3 0 0 3 2. DS7202 Radar Signal Processing 3 0 0 3 3. VL7101 VLSI Signal Processing 3 0 0 3 4. CP7103 Multicore Architectures 3 0 0 3 5. Elective II 3 0 0 3 6. Elective III 3 0 0 3 PRACTICAL 7. DS7211 Digital Signal Processing Laboratory II 0 0 3 2 TOTAL 18 0 3 20
Transcript of ANNA UNIVERSITY, CHENNAI AFFILIATED INSTITUTIONS...
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ANNA UNIVERSITY, CHENNAIAFFILIATED INSTITUTIONS
REGULATIONS - 2013M.E. DIGITAL SIGNAL PROCESSING
CURRICULUM I TO IV SEMESTERS (FULL TIME)
SEMESTER I
SL.NO
COURSECODE
COURSE TITLE L T P C
THEORY
1. MA7159 Applied Mathematics for Signal ProcessingEngineers
3 1 0 4
2. DS7101 DSP Processor Architecture and Programming 3 0 0 3
3. AP7101 Advanced Digital Signal Processing 3 1 0 4
4. DS7102 Video Compression Techniques 3 0 0 3
5. CU7102 Advanced Digital Communication Techniques 3 0 0 3
6. Elective - I 3 0 0 3
PRACTICAL
7. DS7111 Digital Signal Processing Laboratory I 0 0 3 2
TOTAL 18 2 3 22
SEMESTER II
SL.NO
COURSECODE
COURSE TITLE L T P C
THEORY
1. DS7201 Advanced Digital Image Processing 3 0 0 3
2. DS7202 Radar Signal Processing 3 0 0 3
3. VL7101 VLSI Signal Processing 3 0 0 3
4. CP7103 Multicore Architectures 3 0 0 3
5. Elective II 3 0 0 3
6. Elective III 3 0 0 3
PRACTICAL
7. DS7211 Digital Signal Processing Laboratory II 0 0 3 2
TOTAL 18 0 3 20
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SEMESTER III
SL.NO
COURSECODE COURSE TITLE L T P C
THEORY1. DS7301 Speech and Audio Signal Processing 3 0 0 32. Elective IV 3 0 0 33. Elective V 3 0 0 3PRACTICAL1. DS7311 Project Work (Phase I) 0 0 12 6
TOTAL 9 0 12 15
SEMESTER IV
SL.NO
COURSECODE COURSE TITLE L T P C
PRACTICAL1. DS7411 Project Work (Phase II) 0 0 24 12
TOTAL 0 0 24 12
TOTAL NO.OF CREDITS:69
LIST OF ELECTIVES
ELECTIVE - I
ELECTIVE - II
SL.NO
COURSECODE COURSE TITLE L T P C
1. CU7009 Neural Network and Applications 3 0 0 3
2. IF7301 Soft Computing 3 0 0 3
3. CP7030 Robotics 3 0 0 3
4. AP7102 Advanced Digital Logic System Design 3 0 0 3
SL.NO
COURSECODE COURSE TITLE L T P C
1. DS7001 Optical Signal Processing 3 0 0 3
2. DS7002 Bio Signal Processing 3 0 0 3
3. BM7201 Applied Medical Image Processing 3 0 0 3
4. DS7003 Array Signal Processing 3 0 0 3
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ELECTIVE - III
ELECTIVE - IV
ELECTIVE - V
SL.NO
COURSECODE COURSE TITLE L T P C
ELECTIVES III
1. DS7004 Wireless Sensor Networks 3 0 0 3
2. DS7005 Cryptographic Techniques 3 0 0 3
3. DS7006 Adaptive Signal Processing 3 0 0 3
4. DS7007 Underwater Acoustics Signal Processing 3 0 0 3
SL.NO
COURSECODE COURSE TITLE L T P C
1. DS7008 Embedded System Design 3 0 0 3
2. DS7009 Real Time Operating System Design 3 0 0 3
3. DS7010 Microcontroller System Design and Analysis 3 0 0 3
4. DS7011 Digital Signal Compression 3 0 0 3
5. MU7011 Video Compression 3 0 0 3
SL.NO
COURSECODE COURSE TITLE L T P C
1. MU7102 Multimedia Communication Networks 3 0 0 3
2. CP7204 Advanced Operating systems 3 0 0 3
3. DS7012 Design and Analysis of Algorithms 3 0 0 3
4. DS7013 Digital Modulation and Coding 3 0 0 3
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MA7159 APPLIED MATHEMATICS FOR SIGNAL PROCESSING ENGINEERS L T P C3 1 0 4
OBJECTIVES: To develop the ability to use the concepts of Linear algebra and Special functions for
solving problems related to Networks. To formulate and construct a mathematical model for a linear programming problem in real
life situation; To expose the students to solve ordinary differential equations by various techniques.
OUTCOMES: To achieve an understanding of the basic concepts of algebraic equations and method of
solving them. To familiarize the students with special functions and solve problems associated with
engineering applications.
UNIT I LINEAR ALGEBRA (9+3)Vector spaces – norms – Inner Products – Eigen values using QR transformations – QRfactorization - generalized eigenvectors – Canonical forms – singular value decomposition andapplications - pseudo inverse – least square approximations --Toeplitz matrices and someapplications.
UNIT II SPECIAL FUNCTIONS (9+3)Bessel's equation – Bessel function – Recurrence relations - Generating function and orthogonalproperty for Bessel functions of first kind – Fourier-Bessel expansion.
UNIT IV LINEAR PROGRAMMING (9+3)Formulation – Graphical solution – Simplex method – Two phase method - Transportation andAssignment Models
UNIT IV ALGEBRAIC EQUATIONS (9+3)Systems of linear equations: Gauss Elimination method, pivoting techniques, Thomas algorithm fortridiagonal system – Jacobi, Gauss Seidel, SOR iteration methods - Systems of nonlinearequations: Fixed point iterations, Newton Method, Eigenvalue problems: power method, inversepower method, Faddeev – Leverrier Method.
UNIT V ORDINARY DIFFERENTIAL EQUATIONS (9+3)Runge Kutta Methods for system of IVPs, numerical stability, Adams-Bashforth multistep method,solution of stiff ODEs, shooting method, BVP: Finite difference method, orthogonal collocationmethod, orthogonal collocation with finite element method, Galerkin finite element method.
TOTAL: 45+15=60 PERIODSBOOKS FOR STUDY:1. Richard Bronson, Gabriel B.Costa, “Linear Algebra”, Academic Press, Second Edition, 2007.2. Peter V.O'Neil, “Advanced Engineering Mathematics”, Cengage Learning, Seventh Edition,2011.3. Grimaldi R.P. and Ramana B.V., “Discrete and Combinatorial Mathematics”, Pearson
Education, Reprinted in 2006. (5th Edition).4. Saumyen Guha and Rajesh Srivastava, “Numerical methods for Engineering and
Science”, Oxford Higher Education, New Delhi, 2010.
REFERENCES:1. Richard Bronson, “Matrix Operation”, Schaum’s outline series, Tata McGraw Hill, Second
Edition, 2011.2. Glyn James, “Advanced Modern Engineering Mathematics”, Pearson/Prentice Hall, Third
Edition, 2004.3. Erwin Kreyszig. “Advanced Engineering Mathematics”, John Wiley & Sons, Ninth Edition,
2011.4. Taha H.A., “Operations Research: An introduction”, Pearson Education, Asia, New Delhi,
Ninth Edition, 2012.
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DS7101 DSP PROCESSOR ARCHITECTURE AND PROGRAMMING L T P C3 0 0 3
OBJECTIVES:The objective of this course is to provide in-depth knowledge on
Digital Signal Processor basics Third generation DSP Architecture and programming skills Advanced DSP architectures and some applications.
OUTCOMES:Students should be able to: Become Digital Signal Processor specialized engineer DSP based System Developer
UNIT I FUNDAMENTALS OF PROGRAMMABLE DSPs 9Multiplier and Multiplier accumulator – Modified Bus Structures and Memory access in PDSPs –Multiple access memory – Multi-port memory – VLIW architecture- Pipelining – Special Addressingmodes in P-DSPs – On chip Peripherals.
UNIT II TMS320C5X PROCESSOR 9Architecture – Assembly language syntax - Addressing modes – Assembly language Instructions -Pipeline structure, Operation – Block Diagram of DSP starter kit – Application Programs forprocessing real time signals.
UNIT III TMS320C6X PROCESSOR 9Architecture of the C6x Processor - Instruction Set - DSP Development System: Introduction –DSP Starter Kit Support Tools- Code Composer Studio - Support Files - Programming Examples toTest the DSK Tools – Application Programs for processing real time signals.
UNIT IV ADSP PROCESSORS 9Architecture of ADSP-21XX and ADSP-210XX series of DSP processors- Addressing modes andassembly language instructions – Application programs –Filter design, FFT calculation.
UNIT V ADVANCED PROCESSORS 9Architecture of TMS320C54X: Pipe line operation, Code Composer studio – Architecture ofTMS320C6X - Architecture of Motorola DSP563XX – Comparison of the features of DSP familyprocessors.
TOTAL: 45 PERIODSREFERENCES:1. B.Venkataramani and M.Bhaskar, “Digital Signal Processors – Architecture, Programming and
Applications” – Tata McGraw – Hill Publishing Company Limited. New Delhi, 2003.2. Avtar Singh and S. Srinivasan, Digital Signal Processing – Implementations using DSP
Microprocessors with Examples from TMS320C54xx, cengage Learning India Private Limited,Delhi 2012
3. User guides Texas Instrumentation, Analog Devices, Motorola.4. Rulph Chassaing, Digital Signal Processing and Applications with the C6713 and C6416 DSK,
A JOHN WILEY & SONS, INC., PUBLICATION, 2005
AP7101 ADVANCED DIGITAL SIGNAL PROCESSING L T P C3 1 0 4
OBJECTIVES:The purpose of this course is to provide in-depth treatment on methods and techniques in discrete-time signal transforms, digital filter design, optimal filtering
Power spectrum estimation, multi-rate digital signal processing DSP architectures which are of importance in the areas of signal processing, control and
communications.
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OUTCOMES:Students should be able to: To design adaptive filters for a given application To design multirate DSP systems.
UNIT I DISCRETE RANDOM SIGNAL PROCESSING 9Weiner Khitchine relation - Power spectral density – filtering random process, SpectralFactorization Theorem, special types of random process – Signal modeling-Least Squares method,Pade approximation, Prony’s method, iterative Prefiltering, Finite Data records, Stochastic Models.
UNIT II SPECTRUM ESTIMATION 9Non-Parametric methods - Correlation method - Co-variance estimator - Performance analysis ofestimators – Unbiased consistent estimators - Periodogram estimator - Barlett spectrum estimation- Welch estimation - Model based approach - AR, MA, ARMA Signal modeling - Parameterestimation using Yule-Walker method.
UNIT III LINEAR ESTIMATION AND PREDICTION 9Maximum likelihood criterion - Efficiency of estimator - Least mean squared error criterion - Wienerfilter - Discrete Wiener Hoff equations - Recursive estimators - Kalman filter - Linear prediction,Prediction error - Whitening filter, Inverse filter - Levinson recursion, Lattice realization, Levinsonrecursion algorithm for solving Toeplitz system of equations.
UNIT IV ADAPTIVE FILTERS 9FIR Adaptive filters - Newton's steepest descent method - Adaptive filters based on steepestdescent method - Widrow Hoff LMS Adaptive algorithm - Adaptive channel equalization - Adaptiveecho canceller - Adaptive noise cancellation - RLS Adaptive filters - Exponentially weighted RLS -Sliding window RLS - Simplified IIR LMS Adaptive filter.
UNIT V MULTIRATE DIGITAL SIGNAL PROCESSING 9Mathematical description of change of sampling rate - Interpolation and Decimation - Continuoustime model - Direct digital domain approach - Decimation by integer factor - Interpolation by aninteger factor - Single and multistage realization - Poly phase realization - Applications to sub bandcoding - Wavelet transform and filter bank implementation of wavelet expansion of signals.
L +T= 45+15=60 PERIODSREFERENCES:1. Monson H. Hayes, “Statistical Digital Signal Processing and Modeling”, John Wiley and Sons
Inc., New York, 2006.2. Sophoncles J. Orfanidis, “Optimum Signal Processing “, McGraw-Hill, 2000.3. John G. Proakis, Dimitris G. Manolakis, “Digital Signal Processing”, Prentice Hall of India, New
Delhi, 2005.4. Simon Haykin, “Adaptive Filter Theory”, Prentice Hall, Englehood Cliffs, NJ1986.5. S. Kay,” Modern spectrum Estimation theory and application”, Prentice Hall, Englehood Cliffs,
NJ1988.6. P. P. Vaidyanathan, “Multirate Systems and Filter Banks”, Prentice Hall, 1992.
DS7102 VIDEO COMPRESSION TECHNIQUES L T P C3 0 0 3
OBJECTIVES: To understand the basics of Video representation in space and transform domains To understand intraframe coding Techniques To understand interframe compression To know the basics of Video Compression standards
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OUTCOMES: To be able to design Video Compression schemes To be able to implement the state-of the art Video Standards
UNIT I DIGITAL VIDEO REPRESENTATION 9Video Frames and Fields- Colour Spaces- Spatial and Temporal sampling-Quantization – MSE-Uniform Scalar Quanitization- Non-Uniform Scalar Quantization- Transform Representation –2D_DFT- DCT- 2D Wavelet Transform
UNIT II STILL IMAGE COMPRESSION TECHNIQUES 9Spatial Redundancy Reduction- Predictive Coding - Transform Coding Techniques-VariableLength Coding_ Huffman Coding – Arithmetic Coding –Run Length Coding -Still ImageCompression-JPEG
UNIT III VIDEO COMPRESSION 9Inter_frame Coding- I, P,B and D frames-Motion estimation – Motion estimation with Half PixelPrecision-Bidirectional Motion estimation- MPEG2-Scalability
UNIT IV LOW BIT RATE VIDEO CODING 9Coding for Video Conferencing – Overview of H.261 – Coding in H.263- Coding of Motion Vectors
UNIT V CONTENT BASED VIDEO CODING 9Video Object Plane-encoding of VOPs-Segmenation-shape Coding- Texture Coding-MPEG-4 –Basics of content description, search and delivery in MPEG7
TOTAL: 45 PERIODSREFERENCES1. Mohammed Ghanbari, Standard Codecs: Image compression to Advanced Video Coding,
Telecommunication Series, IET, 20082. Peter Symes, Digital Video Compression, McGraw Hill, 20043. Iain E.G. Richardson, H.264 and MPEG-4, Video Compression: Video Coding for Next
generation Multimedia, John Wiley, 2003.
CU7102 ADVANCED DIGITAL COMMUNICATION TECHNIQUES L T P C3 0 0 3
OBJECTIVES: To understand the basics of signal-space analysis and digital transmission. To understand the coherent and noncoherent receivers and its impact on different channel
characteristics. To understand Orthogonal Frequency Division Multiplexing. To understand the different block coded and convolutional coded digital communication
systems. To understand the different Equalizers.
UNIT I COHERENT AND NON-COHERENT COMMUNICATION 9Coherent receivers – Optimum receivers in WGN – IQ modulation & demodulation – Noncoherentreceivers in random phase channels; MFSK receivers – Rayleigh and Rician channels – Partiallycoherent receivers – DPSK; M-PSK; M-DPSK--BER Performance Analysis. CarrierSynchronization- Bit synchronization.
UNIT II EQUALIZATION TECHNIQUES 9Band Limited Channels- ISI – Nyquist Criterion- Controlled ISI-Partial Response signals-Equalization algorithms – Viterbi Algorithm – Linear equalizer – Decision feedback equalization –Adaptive Equalization algorithms.
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UNIT III BLOCK CODED DIGITAL COMMUNICATION 9Architecture and performance – Binary block codes; Orthogonal; Biorthogonal; Transorthogonal –Shannon’s channel coding theorem; Channel capacity; Matched filter; Concepts of Spreadspectrum communication – Coded BPSK and DPSK demodulators– Linear block codes;Hammning; Golay; Cyclic; BCH ; Reed – Solomon codes - Space time block codes
UNIT IV CONVOLUTIONAL CODED DIGITAL COMMUNICATION 9Representation of codes using Polynomial, State diagram, Tree diagram, and Trellis diagram –Decoding techniques using Maximum likelihood, Viterbi algorithm, Sequential and Thresholdmethods – Error probability performance for BPSK and Viterbi algorithm, Turbo Coding.
UNIT V OFDM 9Generation of sub-carriers using the IFFT; Guard Time and Cyclic Extension; Windowing; OFDMsignal processing; Peak Power Problem: PAP reduction schemes- Clipping, Filtering, Coding andScrambling.-
TOTAL: 45 PERIODSOUTCOMES:Upon Completion of the course, the students will be able to
Develop the ability to understand the concepts of signal space analysis coherent and non-coherent receivers.
Comprehend the generation of OFDM signals and the processing of the signals. Possess knowledge on different block codes and convolutional codes. Conceptually appreciate different Equalization techniques.
REFERENCES:1. M.K.Simon, S.M.Hinedi and W.C.Lindsey, Digital communication techniques; Signalling and
detection, Prentice Hall India, New Delhi. 1995.2. Simon Haykin, Digital communications, John Wiley and sons, 19983. Bernard Sklar., ‘Digital Communications’, second edition, Pearson Education,2001.4. John G. Proakis., ‘Digital Communication’, 4 th edition, Mc Graw Hill Publication, 20015. Theodore S.Rappaport., ‘Wireless Communications’, 2nd edition, Pearson Education, 2002.6. Stephen G. Wilson., ‘Digital Modulation and Coding’, First Indian Reprint ,Pearson Education,
2003.7. Richard Van Nee & Ramjee Prasad., ‘OFDM for Multimedia Communications’ Artech House
Publication,2001.
DS7111 DIGITAL SIGNAL PROCESSING LABORATORY I L T P C0 0 3 2
Using Digital Signal Processing Starter Kit1. Sine wave Generation Using Eight Points with DIP Switch Control2. Sine wave Generation with Two Sliders for Amplitude and Frequency Control3. Square, Ramp Generation Using a Lookup Table4. Loop Program with Stereo Input and Stereo Output5. Program to generate Echo with controls for different effects6. Pseudorandom noise sequence generation program7. Implementation of Four Different Filters: Low pass, High pass, Band pass, and Band stop8. FIR Implementation Using C Calling an ASM Function with a Circular Buffer9. IIR Filter Implementation Using Second-Order Stages in Cascade
TOTAL: 45 PERIODS
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DS7201 ADVANCED DIGITAL IMAGE PROCESSING L T P C3 0 0 3
OBJECTIVES: To understand the image fundamentals and mathematical transforms necessary for image
processing and to study the image enhancement techniques. To understand the image segmentation and representation techniques. To understand how image are analyzed to extract features of interest. To introduce the concepts of image registration and image fusion. To analyze the constraints in image processing when dealing with 3D data sets.
UNIT I FUNDAMENTALS OF DIGITAL IMAGE PROCESSING 9Elements of visual perception, brightness, contrast, hue, saturation, mach band effect, 2D imagetransforms-DFT, DCT, KLT, and SVD. Image enhancement in spatial and frequency domain,Review of morphological image processing
UNIT II SEGMENTATION 9Edge detection, Thresholding, Region growing, Fuzzy clustering, Watershed algorithm, Activecontour methods, Texture feature based segmentation, Model based segmentation, Atlas basedsegmentation, Wavelet based Segmentation methods
UNIT III FEATURE EXTRACTION 9First and second order edge detection operators, Phase congruency, Localized feature extraction-detecting image curvature, shape features Hough transform, shape skeletonization, Boundarydescriptors, Moments, Texture descriptors- Autocorrelation, Co-occurrence features, Runlengthfeatures, Fractal model based features, Gabor filter, wavelet features.
UNIT IV REGISTRATION AND IMAGE FUSION 9Registration- Preprocessing, Feature selection-points, lines, regions and templates Featurecorrespondence-Point pattern matching, Line matching, region matching Template matching.Transformation functions-Similarity transformationand Affine Transformation. Resampling- NearestNeighbour and Cubic SplinesImage Fusion-Overview of image fusion, pixel fusion, Multiresolution based fusiondiscrete wavelettransform, Curvelet transform. Region based fusion.
UNIT V 3D IMAGE VISUALIZATION 9Sources of 3D Data sets, Slicing the Data set, Arbitrary section planes, The use of color,Volumetric display, Stereo Viewing, Ray tracing, Reflection, Surfaces, Multiply connected surfaces,Image processing in 3D, Measurements on 3D images.
TOTAL: 45 PERIODS
OUTCOMES:Upon Completion of the course, the students will be able to To understand image formation and the role human visual system plays in perception of gray
and color image data. To apply image processing techniques in both the spatial and frequency (Fourier) domains. To design image analysis techniques in the form of image segmentation and to evaluate the
methodologies for segmentation. To conduct independent study and analysis of feature extraction techniques. To understand the concepts of image registration and image fusion. To analyze the constraints in image processing when dealing with 3D data sets and to apply
image processing algorithms in practical applications.
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TEXT BOOKS:1. John C.Russ, “The Image Processing Handbook”, CRC Press, 2007.2. Mark Nixon, Alberto Aguado, “Feature Extraction and Image Processing”, Academic Press,
2008.3. Ardeshir Goshtasby, “2D and 3D Image registration for Medical, Remote Sensing and Industrial
Applications”, John Wiley and Sons, 2005.
REFERENCES:1. Rafael C. Gonzalez, Richard E. Woods, , Digital Image Processing', Pearson, Education, Inc.,
Second Edition, 2004.2. Anil K. Jain, , Fundamentals of Digital Image Processing', Pearson Education, Inc., 2002.3. Rick S.Blum, Zheng Liu,“ Multisensor image fusion and its Applications“, Taylor & Francis,
2006.
DS7202 RADAR SIGNAL PROCESSING L T P C3 0 0 3
OBJECTIVES: To understand the Radar Signal acquisition and sampling in multiple domains To provide clear instruction in radar DSP basics To equip the skills needed in both design and analysis of common radar algorithms To understand the basics of synthetic aperture imaging and adaptive array processing To illustrate how theoretical results are derived and applied in practice
UNIT I INTRODUCTION TO RADAR SYSTEMS 9History and application of radar, basic radar function, elements of pulsed radar,review of signalprocessing concepts and operations, A preview of basic radar signal processing, radar systemcomponents, advanced radar signal processing
UNIT II SIGNAL MODELS 9Components of a radar signal, amplitude models,types of clutters,noise model and signal-to-noiseratio, jamming, frequency models: the doppler shift, spatial models,spectral model
UNIT III SAMPLING AND QUANTIZATION OF PULSED RADAR SIGNALS 9Domains and criteria for sampling radar signals, Sampling in the fast time dimension,Sampling inslow time: selecting the pulse repetition interval,sampling the doppler spectrum,Sampling in thespatial and angle dimension, Quantization, I/Q Imbalance and Digital I/Q.
UNIT IV RADAR WAVEFORMS 9Introduction, The waveform matched filter, Matched filtering of moving targets, The ambiguityfunction, The pulse burst waveform, frequency-modulated pulse compression waveforms, Rangesidelobe control for FM waveforms, the stepped frequency waveform, Phase-modulated pulsecompression waveforms, COSTAS Frequency Codes.
UNIT V DOPPLER PROCESSING 9Alternate forms of the Doppler spectrum, Moving target indication (MTI), Pulse Doppler processing,dwell-to-dwell stagger, Pulse pair processing, additional Doppler processing issues, cluttermapping and the moving target detector, MTI for moving platforms: adaptive displaced phasecenter antenna processing
TOTAL: 45 PERIODS
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REFERENCES:1. Fundamentals of Radar Signal Processing, Mark A. Richards McGraw-Hill, New York, 20052. Principles of Radar and Sonar Signal Processing, Francois Le Chevalier, Artech House.3. Radar systems, Peak Detection and Tracking, Michael O Kolawole, 2010, Elseveir4. Introduction To Radar Systems 3/E, Skolnik, McGraw Hill.5. Radar Principles, Peyton Z. Peebles, 2009 Wiley India6. Radar Design Principles-Signal Processing and the environment, Fred E. Nathanson, PHI
VL7101 VLSI SIGNAL PROCESSING L T P C3 0 0 3
OBJECTIVES To understand the various VLSI architectures for digital signal processing. To know the techniques of critical path and algorithmic strength reduction in the filter structures. To study the performance parameters, viz. area, speed and power.
OUTCOMES To be able to design architectures for DSP algorithms. To be able to optimize design in terms of area, speed and power. To be able to incorporate pipeline based architectures in the design. To be able to carry out HDL simulation of various DSP algorithms.
UNIT I INTRODUCTION 6Overview of DSP – FPGA Technology – DSP Technology requirements – Design Implementation.
UNIT II METHODS OF CRITICAL PATH REDUCTION 12Binary Adders – Binary Multipliers – Multiply-Accumulator (MAC) and sum of product (SOP) –Pipelining and parallel processing – retiming – unfolding – systolic architecture design.
UNIT III ALGORITHMIC STRENGTH REDUCTION METHODS ANDRECURSIVE FILTER DESIGN 9
Fast convolution-pipelined and parallel processing of recursive and adaptive filters – fast IIR filtersdesign.
UNIT IV DESIGN OF PIPELINED DIGITAL FILTERS 9Designing FIR filters – Digital lattice filter structures – bit level arithmetic architecture – redundantarithmetic – scaling and round-off noise.
UNIT V SYNCHRONOUS ASYNCHRONOUS PIPELININGAND PROGRAMMABLE DSP 9
Numeric strength reduction – synchronous – wave and asynchronous pipelines – low power design– programmable DSPs – DSP architectural features/alternatives for high performance and lowpower.
TOTAL: 45 PERIODSREFERENCES:1. Keshab K.Parhi, “VLSI Digital Signal Processing Systems, Design and Implementation”, John
Wiley, Indian Reprint, 2007.2. U. Meyer – Baese, "Digital Signal Processing with Field Programmable Arrays", Springer,
Second Edition, Indian Reprint, 2007.3. S.Y.Kuang, H.J. White house, T. Kailath, “VLSI and Modern Signal Processing”, Prentice Hall,
1995.
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CP7103 MULTICORE ARCHITECTURES L T P C3 0 0 3
OBJECTIVES To understand the recent trends in the field of Computer Architecture and identify performance
related parameters To appreciate the need for parallel processing To expose the students to the problems related to multiprocessing To understand the different types of multicore architectures To expose the students to warehouse-scale and embedded architectures
UNIT I FUNDAMENTALS OF QUANTITATIVE DESIGN AND ANALYSIS 9Classes of Computers – Trends in Technology, Power, Energy and Cost – Dependability –Measuring, Reporting and Summarizing Performance – Quantitative Principles of Computer Design– Classes of Parallelism - ILP, DLP, TLP and RLP - Multithreading - SMT and CMP Architectures –Limitations of Single Core Processors - The Multicore era – Case Studies of MulticoreArchitectures.
UNIT II DLP IN VECTOR, SIMD AND GPU ARCHITECTURES 9Vector Architecture - SIMD Instruction Set Extensions for Multimedia – Graphics Processing Units -Detecting and Enhancing Loop Level Parallelism - Case Studies.
UNIT III TLP AND MULTIPROCESSORS 9Symmetric and Distributed Shared Memory Architectures – Cache Coherence Issues -Performance Issues – Synchronization Issues – Models of Memory Consistency - InterconnectionNetworks – Buses, Crossbar and Multi-stage Interconnection Networks.
UNIT IV RLP AND DLP IN WAREHOUSE-SCALE ARCHITECTURES 9Programming Models and Workloads for Warehouse-Scale Computers – Architectures forWarehouse-Scale Computing – Physical Infrastructure and Costs – Cloud Computing – CaseStudies.
UNIT V ARCHITECTURES FOR EMBEDDED SYSTEMS 9Features and Requirements of Embedded Systems – Signal Processing and EmbeddedApplications – The Digital Signal Processor – Embedded Multiprocessors - Case Studies.
TOTAL: 45 PERIODSOUTCOMES:Upon completion of the course, the students will be able to
Identify the limitations of ILP and the need for multicore architectures Discuss the issues related to multiprocessing and suggest solutions Point out the salient features of different multicore architectures and how they exploit
parallelism Critically analyze the different types of inter connection networks Discuss the architecture of GPUs, warehouse-scale computers and embedded processors
REFERENCES:1. John L. Hennessey and David A. Patterson, “ Computer Architecture – A Quantitative
Approach”, Morgan Kaufmann / Elsevier, 5th edition, 2012.2. Kai Hwang, “Advanced Computer Architecture”, Tata McGraw-Hill Education, 20033. Richard Y. Kain, “Advanced Computer Architecture a Systems Design Approach”, Prentice
Hall, 2011.4. David E. Culler, Jaswinder Pal Singh, “Parallel Computing Architecture : A Hardware/ Software
Approach” , Morgan Kaufmann / Elsevier, 1997.
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DS7211 DIGITAL SIGNAL PROCESSING LABORATORY II L T P C0 0 3 2
USING SIMULINK-DSP STARTER KIT1. MATLAB–DSK Interface Using RTDX2. MATLAB–DSK Interface Using RTDX for FIR Filter Implementation3. Adaptive Filter for Sinusoidal Noise Cancellation4. Adaptive Predictor for Cancellation of Narrowband Interference Added to a Desired Wideband
Signal5. DSK Interface Using RTDX with MATLAB Functions for FFT and Plotting6. mini-project based on the Matlab/Simulink-DSK
TOTAL : 45 PERIODS
DS7301 SPEECH AND AUDIO SIGNAL PROCESSING L T P C3 0 0 3
OBJECTIVES:1. To study the basic concepts of speech and audio.2. To study the analysis of various M-band filter banks for audio coding3. To learn various transform coders for audio coding.4. To study the speech processing methods in time and frequency domain
UNIT I MECHANICS OF SPEECH AND AUDIO 9Introduction - Review Of Signal Processing Theory-Speech production mechanism – Nature ofSpeech signal – Discrete time modelling of Speech production – Classification of Speech sounds –Phones – Phonemes – Phonetic and Phonemic alphabets – Articulatory features.Absolute Threshold of Hearing - Critical Bands- Simultaneous Masking, Masking-Asymmetry, andthe Spread of Masking- Nonsimultaneous Masking - Perceptual Entropy - Basic measuringphilosophy -Subjective versus objective perceptual testing - The perceptual audio quality measure(PAQM) - Cognitive effects in judging audio quality.
UNIT II TIME-FREQUENCY ANALYSIS: FILTER BANKSAND TRANSFORMS 9
Introduction -Analysis-Synthesis Framework for M-band Filter Banks- Filter Banks for AudioCoding: Design Considerations - Quadrature Mirror and Conjugate Quadrature Filters- Tree-Structured QMF and CQF M-band Banks - Cosine Modulated “Pseudo QMF” M-band Banks -Cosine Modulated Perfect Reconstruction (PR) M-band Banksand the Modified Discrete CosineTransform (MDCT) - Discrete Fourier and Discrete Cosine Transform - Pre-echo Distortion- Pre-echo Control Strategies.
UNIT III AUDIO CODING AND TRANSFORM CODERS 9LosslessAudioCoding-LossyAudioCoding- ISO-MPEG-1A,2A,2A Advaned , 4A AudioCoding -Optimum Coding in the Frequency Domain - Perceptual Transform Coder -Brandenburg-JohnstonHybrid Coder - CNET Coders - Adaptive Spectral Entropy Coding -Differential Perceptual AudioCoder - DFT Noise Substitution -DCT with Vector Quantization -MDCT with Vector Quantization.
UNIT IV TIME AND FREQUENCY DOMAIN METHODS FORSPEECH PROCESSING 9
Time domain parameters of Speech signal – Methods for extracting the parameters :Energy,Average Magnitude – Zero crossing Rate – Silence Discrimination using ZCRand energyShort Time Fourier analysis – Formant extraction – Pitch Extraction using time and frequencydomain methods
HOMOMORPHIC SPEECH ANALYSIS:Cepstral analysis of Speech – Formant and Pitch Estimation – Homomorphic Vocoders.
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UNIT V LINEAR PREDICTIVE ANALYSIS OF SPEECH 9Formulation of Linear Prediction problem in Time Domain – Basic Principle – Auto correlationmethod – Covariance method – Solution of LPC equations – Cholesky method – Durbin’sRecursive algorithm – lattice formation and solutions – Comparison of different methods –Application of LPC parameters – Pitch detection using LPC parameters – Formant analysis –VELP – CELP.
TOTAL : 45 PERIODSREFERENCES:1. Digital Audio Signal Processing, Second Edition, Udo Zölzer, A John Wiley& sons Ltd
Publicatioons2. Applications of Digital Signal Processing to Audio And Acoustics
Mark Kahrs, Karlheinz Brandenburg, KLUWER ACADEMIC PUBLISHERS NEW YORK,BOSTON, DORDRECHT, L ONDON , MOSCOW
3. Digital Processing of Speech signals – L.R.Rabiner and R.W.Schaffer - Prentice Hall --1978
CU7009 NEURAL NETWORK AND APPLICATIONS L T P C3 0 0 3
UNIT I BASIC LEARNING ALGORITHMS 9Biological Neuron – Artificial Neural Model - Types of activation functions – Architecture:Feedforward and Feedback – Learning Process: Error Correction Learning –Memory BasedLearning – Hebbian Learning – Competitive Learning - Boltzman Learning – Supervised andUnsupervised Learning – Learning Tasks: Pattern Space – Weight Space – Pattern Association –Pattern Recognition – Function Approximation – Control – Filtering - Beamforming – Memory –Adaptation - Statistical Learning Theory – Single Layer Perceptron – Perceptron LearningAlgorithm – Perceptron Convergence Theorem – Least Mean Square Learning Algorithm –Multilayer Perceptron – Back Propagation Algorithm – XOR problem – Limitations of BackPropagation Algorithm.
UNIT II RADIAL-BASIS FUNCTION NETWORKS AND SUPPORT VECTORMACHINES RADIAL BASIS FUNCTION NETWORKS 9
Cover’s Theorem on the Separability of Patterns - Exact Interpolator – Regularization Theory –Generalized Radial Basis Function Networks - Learning in Radial Basis Function NetworksApplications: XOR Problem – Image Classification.
SUPPORT VECTOR MACHINESOptimal Hyperplane for Linearly Separable Patterns and Nonseparable Patterns – Support VectorMachine for Pattern Recognition – XOR Problem - -insensitive Loss Function –Support VectorMachines for Nonlinear Regression
UNIT III COMMITTEE MACHINES 9Ensemble Averaging - Boosting – Associative Gaussian Mixture Model – Hierarchical Mixture ofExperts Model(HME) – Model Selection using a Standard Decision Tree – A Priori andPostprioriProbabilities – Maximum Likelihood Estimation – Learning Strategies for the HME Model -EMAlgorithm – Applications of EM Algorithm to HME Model
NEURODYNAMICS SYSTEMSDynamical Systems – Attractors and Stability – Non-linear Dynamical Systems- Lyapunov Stability– Neurodynamical Systems – The Cohen-Grossberg Ttheorem.
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UNIT IV ATTRACTOR NEURAL NETWORKS: 9Associative Learning – Attractor Neural Network Associative Memory – Linear Associative Memory– Hopfield Network – Content Addressable Memory – Strange Attractors and Chaos- ErrorPerformance of Hopfield Networks - Applications of Hopfield Networks – Simulated Annealing –Boltzmann Machine – Bidirectional Associative Memory – BAM Stability Analysis – Error Correctionin BAMs - Memory Annihilation of Structured Maps in BAMS – Continuous BAMs – Adaptive BAMs– Applications
ADAPTIVE RESONANCE THEORY:Noise-Saturation Dilemma - Solving Noise-Saturation Dilemma – Recurrent On-center – Offsurround Networks – Building Blocks of Adaptive Resonance – Substrate of Resonance StructuralDetails of Resonance Model – Adaptive Resonance Theory – Applications
UNIT V SELF ORGANISING MAPS 9Self-organizing Map – Maximal Eigenvector Filtering – Sanger’s Rule – Generalized Learning Law– Competitive Learning - Vector Quantization – Mexican Hat Networks – Self -organizing FeatureMaps – Applications
PULSED NEURON MODELS:Spiking Neuron Model – Integrate-and-Fire Neurons – Conductance Based Models – Computingwith Spiking Neurons.
TOTAL: 45 PERIODSREFERENCES:1. Satish Kumar, “Neural Networks: A Classroom Approach”, Tata McGraw-Hill Publishing
Company Limited, New Delhi, 2004.2. Simon Haykin, “Neural Networks: A Comprehensive Foundation”, 2ed., Addison Wesley
Longman (Singapore) Private Limited, Delhi, 2001.3. Martin T.Hagan, Howard B. Demuth, and Mark Beale, “Neural Network Design”, Thomson
Learning, New Delhi, 2003.4. James A. Freeman and David M. Skapura, “Neural Networks Algorithms, Applications, and
Programming Techniques, Pearson Education (Singapore) Private Limited, Delhi, 2003.
IF7301 SOFT COMPUTING L T P C3 0 0 3
OBJECTIVES To learn the key aspects of Soft computing and Neural networks. To know about the components and building block hypothesis of Genetic algorithm. To understand the features of neural network and its applications To study the fuzzy logic components To gain insight onto Neuro Fuzzy modeling and control. To gain knowledge in machine learning through Support vector machines.
UNIT I INTRODUCTION TO SOFT COMPUTING 9Evolution of Computing - Soft Computing Constituents – From Conventional AI to ComputationalIntelligence - Machine Learning Basics
UNIT II GENETIC ALGORITHMS 9Introduction, Building block hypothesis, working principle, Basic operators and Terminologies likeindividual, gene, encoding, fitness function and reproduction, Genetic modeling: Significance ofGenetic operators, Inheritance operator, cross over, inversion & deletion, mutation operator,Bitwise operator, GA optimization problems, JSPP (Job Shop Scheduling Problem), TSP(Travelling Salesman Problem),Differences & similarities between GA & other traditional methods,Applications of GA.
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UNIT III NEURAL NETWORKS 9Machine Learning using Neural Network, Adaptive Networks – Feed Forward Networks– Supervised Learning Neural Networks – Radial Basis Function Networks - ReinforcementLearning – Unsupervised Learning Neural Networks – Adaptive Resonance Architectures –Advances in Neural Networks.
UNIT IV FUZZY LOGIC 9Fuzzy Sets – Operations on Fuzzy Sets – Fuzzy Relations – Membership Functions-Fuzzy Rulesand Fuzzy Reasoning – Fuzzy Inference Systems – Fuzzy Expert Systems – Fuzzy DecisionMaking
UNIT V NEURO-FUZZY MODELING 9Adaptive Neuro-Fuzzy Inference Systems – Coactive Neuro-Fuzzy Modeling – Classification andRegression Trees – Data Clustering Algorithms – Rule base Structure Identification – Neuro-FuzzyControl – Case Studies.
TOTAL : 45 PERIODSOUTCOMES
Implement machine learning through Neural networks. Develop a Fuzzy expert system. Model Neuro Fuzzy system for clustering and classification. Write Genetic Algorithm to solve the optimization problem
REFERENCES:1. Jyh-Shing Roger Jang, Chuen-Tsai Sun, Eiji Mizutani, “Neuro-Fuzzy and Soft Computing”,
Prentice-Hall of India, 2003.2. Kwang H.Lee, “First course on Fuzzy Theory and Applications”, Springer–Verlag Berlin
Heidelberg, 2005.3. George J. Klir and Bo Yuan, “Fuzzy Sets and Fuzzy Logic-Theory and Applications”, Prentice
Hall, 1995.4. James A. Freeman and David M. Skapura, “Neural Networks Algorithms, Applications, and
Programming Techniques”, Pearson Edn., 2003.5. David E. Goldberg, “Genetic Algorithms in Search, Optimization and Machine Learning”,
Addison Wesley, 2007.6. Mitsuo Gen and Runwei Cheng,”Genetic Algorithms and Engineering Optimization”, Wiley
Publishers 2000.7. Mitchell Melanie, “An Introduction to Genetic Algorithm”, Prentice Hall, 1998.8. S.N.Sivanandam, S.N.Deepa, “Introduction to Genetic Algorithms”, Springer, 2007.9. Eiben and Smith “Introduction to Evolutionary Computing” Springer10. E. Sanchez, T. Shibata, and L. A. Zadeh, Eds., "Genetic Algorithms and Fuzzy Logic Systems:
Soft Computing Perspectives, Advances in Fuzzy Systems - Applications and Theory", Vol. 7,River Edge, World Scientific, 1997.
CP7030 ROBOTICS L T P C3 0 0 3
OBJECTIVES To understand robot locomotion and mobile robot kinematics To understand perception in robotics To understand mobile robot localization To understand mobile robot mapping To understand simultaneous localization and mapping (SLAM) To understand robot planning and navigation
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UNIT I LOCOMOTION AND KINEMATICS 9Introduction to Robotics – key issues in robot locomotion – legged robots – wheeled mobile robots– aerial mobile robots – introduction to kinematics – kinematics models and constraints – robotmaneuverability
UNIT II ROBOT PERCEPTION 9Sensors for mobile robots – vision for robotics – cameras – image formation – structure from stereo– structure from motion – optical flow – color tracking – place recognition – range data
UNIT III MOBILE ROBOT LOCALIZATION 9Introduction to localization – challenges in localization – localization and navigation – beliefrepresentation – map representation – probabilistic map-based localization – Markov localization –EKF localization – UKF localization – Grid localization – Monte Carlo localization – localization indynamic environments
UNIT IV MOBILE ROBOT MAPPING 9Autonomous map building – occupancy grip mapping – MAP occupancy mapping – SLAM –extended Kalman Filter SLAM – graph-based SLAM – particle filter SLAM – sparse extendedinformation filter – fastSLAM algorithm
UNIT V PLANNING AND NAVIGATION 9Introduction to planning and navigation – planning and reacting – path planning – obstacleavoidance techniques – navigation architectures – basic exploration algorithms
TOTAL : 45 PERIODSOUTCOMESUpon Completion of the course, the students will be able to
Explain robot locomotion Apply kinematics models and constraints Implement vision algorithms for robotics Implement robot localization techniques Implement robot mapping techniques Implement SLAM algorithms Explain planning and navigation in robotics
REFERENCES:1. Roland Seigwart, Illah Reza Nourbakhsh, and Davide Scaramuzza, “Introduction to
autonomous mobile robots”, Second Edition, MIT Press, 2011.2. Sebastian Thrun, Wolfram Burgard, and Dieter Fox, “Probabilistic Robotics”, MIT Press, 2005.3. Howie Choset et al., “Principles of Robot Motion: Theory, Algorithms, and Implementations”, A
Bradford Book, 2005.4. Gregory Dudek andMichael Jenkin, “Computational Principles of Mobile Robotics”, Second
Edition, Cambridge University Press, 2010.5. Maja J. Mataric, “The Robotics Primer”, MIT Press, 2007.
AP7102 ADVANCED DIGITAL LOGIC SYSTEM DESIGN L T P C3 0 0 3
OBJECTIVES: To analyze synchronous and asynchronous sequential circuits To realize and design hazard free circuits To familiarize the practical issues of sequential circuit design To gain knowledge about different fault diagnosis and testing methods To estimate the performance of digital systems To know about timing analysis of memory and PLD
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UNIT I SEQUENTIAL CIRCUIT DESIGN 9Analysis of Clocked Synchronous Sequential Networks (CSSN) - Modeling of CSSN –State Assignment and Reduction – Design of CSSN – Design of Iterative Circuits– ASM Chart – ASM Realization, Design of Arithmetic circuits for Fast adder- ArrayMultiplier.
UNIT II ASYNCHRONOUS SEQUENTIAL CIRCUIT DESIGN 9Analysis of Asynchronous Sequential Circuit (ASC) – Flow Table Reduction – Races inASC – State Assignment Problem and the Transition Table – Design of ASC – Staticand Dynamic Hazards – Essential Hazards – Design of Hazard free circuits - Data Synchronizers –Designing Vending Machine Controller – Mixed Operating Mode Asynchronous Circuits. Practicalissues such as clock skew, synchronous and asynchronous inputs and switch bouncing.
UNIT III FAULT DIAGNOSIS & TESTING 9Fault diagnosis: Fault Table Method – Path Sensitization Method – Boolean Difference Method –Kohavi Algorithm – Tolerance Techniques – The Compact Algorithm. Design for testability: TestGeneration – Masking Cycle – DFT Schemes. Circuit testing fault model, specific and randomfaults, testing of sequential circuits, Built in Self Test, Built in Logic Block observer (BILBO),signature analysis.
UNIT IV PERFORMANCE ESTIMATION 9Estimating digital system reliability, transmission lines, reflections and terminations, systemintegrity, network issues for digital systems, formal verifications of digital system: model-checking,binary decision diagram, theorem proving, circuit equivalence.
UNIT V TIMING ANALYSIS 9ROM timings, Static RAM timing, Synchronous Static RAM and it’s timing, Dynamic RAM timing,Complex Programmable Logic Devices, Logic Analyzer Basic Architecture, Internal structure, Datadisplay, Setup and Control, Clocking and Sampling.
TOTAL:45 PERIODSREFERENCES:1. Charles H.Roth Jr “Fundamentals of Logic Design”, Thomson Learning 2004.2. Nripendra N Biswas “Logic Design Theory” Prentice Hall of India, 2001.3. Parag K.Lala “An introduction to Logic Circuit Testing” Morgan and claypool publishers, 2009.4. Stephen D Brown, “Fundamentals of digital logic”, TMH publication, 2007.5. Balabanian, “Digital Logic Design Principles”, Wiley publication, 2007.6. Stalling, “Computer Organization & Architecture”, Pearson Education India, 2008.7. J.F.Wakerly, “Digital Design”, Pearson Education India, 2012.8. J.F.Wakerly, “Digital Design principles and practices”, PHI publications, 2005.9. Charles J. Sipil, Microcomputer Handbook McCrindle- Collins Publications 1977.
DS7001 OPTICAL SIGNAL PROCESSING L T P C3 0 0 3
UNIT I ANALYSIS OF TWO DIMENSIONAL SIGNALS AND SYSTEMS 9Review of one-dimensional fourier analysis, analysis of two-dimensional signals and systems,fourier analysis in two dimensions, localization, linear systems and fourier analysis, twodimensional sampling theory.
UNIT II FOUNDATIONS OF SCALAR DIFFRACTION THEORY 9Kirchoff and rayleigh-sommerfield formulations, comparison of kirchoff and Rayleigh sommerfieldtheories, huygens-fresnel principle, non-monochromatic waves, diffraction at boundaries, angularspectrum of plane waves fresnel and fraunhofer diffraction Fresnel approximation, fraunhoferapproximation, examples of fraunhofer diffraction patterns, examples of fresnel diffractioncalculations.
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UNIT III WAVE OPTICS ANALYSIS OF COHERENT OPTICAL SYSTEMS 9Thin lens as phase transformation, fourier transforming properties of lenses, image formationmonochromatic illumination, analysis of complex coherent optical systems.
UNIT IV TRANSFER FUNCTIONS AND FREQUENCY ANALYSIS OF OPTICALIMAGING SYSTEMS 9
Generalized treatment of imaging systems, amplitude transfer function, frequency response forcoherent and incoherent imaging, aberrations and their effect on frequency response, comparisonof coherent and incoherent imaging, resolution beyond classical diffraction limit.
UNIT V WAVEFRONT MODULATION 9Photographic film, liquid crystals and other modulators, diffractive optical elements, analog opticalinformation processing, incoherent image processing systems, coherent optical image processingsystems. Holography- wavefront reconstruction problem, gabor and leith, upatnieks holograms,image locations and magnification, different types of holograms- thick holograms, recordingmaterials, computer-generated holograms, degradation of holographic images, holography withspatially incoherent light, applications.
TOTAL: 45 PERIODSREFERENCES:1. Joseph W. Goodman, “Introduction to Fourier Optics”, 3rd edition, Mc Graw Hill2. D. Casasent, “Optical Data Processing, Applications”, Springer, Verlag, Berlin, 19783. H.J. Caulfield ,“Handbook of holography”, Academic Press New York 19794. P.M. Dufffieux, “The Fourier Transform and its applications to Optics”, John Wiley and sons
1988
DS7002 BIO SIGNAL PROCESSING L T P C3 0 0 3
UNIT I SIGNAL, SYSTEM AND SPECTRUM 9Characteristics of some dynamic biomedical signals, Noises- random, structured and physiologicalnoises. Filters- IIR and FIR filters. Spectrum – power spectral density function, cross-spectraldensity and coherence function, cepstrum and homomorphic filtering. Estimation of mean of finitetime signals.
UNIT II TIME SERIES ANALYSIS AND SPECTRAL ESTIMATION 9Time series analysis – linear prediction models, process order estimation, lattice representation,non stationary process, fixed segmentation, adaptive segmentation, application in EEG, PCGsignals, Time varying analysis of Heart-rate variability, model based ECG simulator. Spectralestimation – Blackman Tukey method, periodogram, and model based estimation. Application inHeart rate variability, PCG signals.
UNIT III ADAPTIVE FILTERING AND WAVELET DETECTION 9Filtering – LMS adaptive filter, adaptive noise canceling in ECG, improved adaptive filtering inFECG, Wavelet detection in ECG – structural features, matched filtering, adaptive waveletdetection, detection of overlapping wavelets.
UNIT IV BIOSIGNAL CLASSIFICATION AND RECOGNITION 9Signal classification and recognition – Statistical signal classification, linear discriminate function,direct feature selection and ordering, Back propagation neural network based classification.Application in Normal versus Ectopic ECG beats.
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UNIT V TIME FREQUENCY AND MULTIVARIATE ANALYSIS 9Time frequency representation, spectrogram, Wigner distribution, Time-scale representation,scalogram, wavelet analysis – Data reduction techniques, ECG data compression, ECGcharacterization, Feature extraction- Wavelet packets, Multivariate component analysis-PCA, ICA
TOTAL: 45 PERIODSREFERNCES:1. Arnon Cohen, Bio-Medical Signal Processing Vol I and Vol II, CRC Press Inc., Boca Rato,
Florida 1999.2. Rangaraj M. Rangayyan, ‘Biomedical Signal Analysis-A case study approach’, Wiley-
Interscience/IEEE Press, 20023. Willis J. Tompkins, Biomedical Digital Signal Processing, Prentice Hall of India, New Delhi,
2003.4. Emmanuel C. Ifeachor, Barrie W.Jervis, ‘Digital Signal processing- A Practical Approach’
Pearson education Ltd., 20025. Raghuveer M. Rao and Ajith S.Bopardikar, Wavelets transform – Introduction to theory and its
applications, Pearson Education, India 2000.
BM7201 APPLIED MEDICAL IMAGE PROCESSING L T P C3 0 0 3
OBJECTIVES: To understand the fundamentals of medical image processing techniques. To develop computational methods and algorithms to analyze and quantify biomedical data
UNIT I IMAGE FUNDAMENTALS AND PRE-PROCESSING 9Image perception, MTF of the visual system, Image fidelity criteria, Image model, Image samplingand quantization – two dimensional sampling theory, Image quantization, Optimum mean squarequantizer, Image transforms – 2D-DFT and other transforms. Image enhancement – pointoperation, Histogram modeling, spatial operations, Transform operations,
UNIT II BASICS OF MEDICAL IMAGE SOURCES 9Radiology- The electromagnetic spectrum-Computed Tomography-Magnetic ResonanceTomography –ultrasound-nuclear medicine and molecular imaging-other imaging techniques-radiation protection and dosimetry.
UNIT III MEDICAL IMAGE REPRESENTATION 9Pixels and voxels – algebraic image operations - gray scale and color representation- depth-colorand look up tables - image file formats- DICOM- other formats- Analyze 7.5, NifTI and Interfile,Image quality and the signal to noise ratio- MATLAB based simple operations.
UNIT IV MEDICAL IMAGE ANALYSIS AND CLASSIFICATION 9Image segmentation- pixel based, edge based, region based segmentation. Image representationand analysis, Feature extraction and representation, Statistical, Shape, Texture, feature and imageclassification – Statistical, Rule based, Neural Network approaches
UNIT V IMAGE REGISTRATIONS AND VISUALIZATION 9Rigid body visualization, Principal axis registration, Interactive principal axis registration, Featurebased registration, Elastic deformation based registration, Image visualization – 2D displaymethods, 3D display methods, virtual reality based interactive visualization.
TOTAL: 45 PERIODS
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OUTCOMES: Students will be able to apply image processing concepts for medical images. Will be able to analyze Morphology, Segmentation techniques and implement these in images. Enables quantitative analysis and visualization of medical images of numerous modalities
such as PET, MRI, CT, or microscopy.
REFERENCES:1. Wolfgang Birkfellner, ‘Applied Medical Image Processing – A Basic course’, CRC Press,
2011.2. Atam P.Dhawan, ‘Medical Image Analysis’, Wiley Interscience Publication, NJ, USA 2003.3. R.C.Gonzalez and R.E.Woods, ‘Digital Image Processing’, Second Edition, Pearson
Education, 2002.4. Anil. K. Jain, ‘Fundamentals of Digital Image Processing’, Pearson education, Indian Reprint
2003.5. Alfred Horowitz, ‘MRI Physics for Radiologists – A Visual Approach’, Second edition Springer
Verlag Network, 1991.6. Kavyan Najarian and Robert Splerstor,” Biomedical signals and Image processing”,CRC –
Taylor and Francis,New York,20067. John L.Semmlow,”Biosignal and Biomedical Image Processing Matlab Based applications”
Marcel Dekker Inc.,New York,20048. Jerry L.Prince and Jnathan M.Links,” Medical Imaging Signals and Systems”- Pearson
Education Inc. 2006.
DS7003 ARRAY SIGNAL PROCESSING L T P C3 0 0 3
UNIT I INTRODUCTION 9Antenna parameters, Basic Antenna elements, Array Fundamentals- Element pattern, directivegain, Directivity, Power Gain, Polarization, array pattern, array gain, array taper efficiency, Pencilbeam array, linear array synthesis-schelknoff ’s polynomial array, binomial array, chebyshev array,Microstrip patch array, Noise in communication.
UNIT II SPATIAL SIGNALS AND SENSOR ARRAYS 9Signals in space and time. Spatial frequency, Direction vs. frequency. Wave fields. Far field andNear field signals. Spatial sampling, Nyquist criterion. Sensor arrays. Uniform linear arrays, planarand random arrays. Array transfer (steering) vector. Array steering vector for ULA. Broadbandarrays.
UNIT III SPATIAL FREQUENCY 9Aliasing in spatial frequency domain. Spatial Frequency Transform, Spatial spectrum. SpatialDomain Filtering, sectorization, switched beam, phased antenna array, adaptive antenna array andadaptive signal processing application, Beam Forming. Spatially white signal. Introduction tomicrophone array signal processing
UNIT IV DIRECTION OF ARRIVAL ESTIMATION 9Non parametric methods - Beam forming and Capon methods. Resolution of Beam formingmethod. Subspace methods - MUSIC, Minimum Norm and ESPRIT techniques. Spatial Smoothing.
UNIT V APPLICATIONS OF ARRAY SIGNAL PROCESSING 9RADAR, Sonar, Seismic, Acoustics, Wireless Communications and networks and Radio Astronomysignal processing applications
TOTAL : 45 PERIODS
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REFERENCES:1. Dan E. Dugeon and Don H. Johnson. Array Signal Processing: Concepts and Techniques.
Prentice Hall. 1993.2. Petre Stoica and Randolph L. Moses, Spectral Analysis of Signals. Prentice Hall. 20053. Simon Haykins and K. J. Ray Liu, Handbook of Array Signal Processing and Sensor Networks,
Wiley.4. Bass J, McPheeters C, Finnigan J, Rodriguez E. Array Signal Processing, February 2005.
DS7004 WIRELESS SENSOR NETWORKS L T P C3 0 0 3
UNIT I INTRODUCTION 9Challenges for wireless sensor networks, Comparison of sensor network with ad hoc network,Single node architecture – Hardware components, energy consumption of sensor nodes, Networkarchitecture – Sensor network scenarios, types of sources and sinks, single hop versus multi-hopnetworks, multiple sinks and sources, design principles, Development of wireless sensor networks.
UNIT II PHYSICAL LAYER 9Introduction, wireless channel and communication fundamentals – frequency allocation,modulation and demodulation, wave propagation effects and noise, channels models, spreadspectrum communication, packet transmission and synchronization, quality of wireless channelsand measures for improvement, physical layer and transceiver design consideration in wirelesssensor networks, Energy usage profile, choice of modulation, Power Management .
UNIT III DATA LINK LAYER 9MAC protocols – fundamentals of wireless MAC protocols, low duty cycle protocols and wakeupconcepts, contention-based protocols, Schedule-based protocols - SMAC, BMAC, Traffic-adaptivemedium access protocol (TRAMA), Link Layer protocols – fundamentals task and requirements,error control, framing, link management.
UNIT IV NETWORK LAYER 9Gossiping and agent-based uni-cast forwarding, Energy-efficient unicast, Broadcast and multicast,geographic routing, mobile nodes, Data-centric routing – SPIN, Directed Diffusion, Energy awarerouting, Gradient-based routing – COUGAR, ACQUIRE, Hierarchical Routing – LEACH, PEGASIS,Location Based Routing – GAF, GEAR, Data aggregation – Various aggregation techniques.
UNIT V CASE STUDY 9Target detection tracking, Habitat monitoring, Environmental disaster monitoring, Practicalimplementation issues, IEEE 802.15.4 low rate WPAN, Operating System Design Issues,Introduction to TinyOS – NesC, Interfaces, modules, configuration, Programming in TinyOS usingNesC, Emulator TOSSIM.
TOTAL:45 PERIODSREFERENCES:1. Kazem Sohraby, Daniel Minoli and Taieb Znati, “ Wireless Sensor Networks Technology-
Protocols and Applications”, John Wiley & Sons, 2007.2. Feng Zhao, Leonidas Guibas, “Wireless Sensor Networks: an information processing
approach”, Else vier publication, 2004.3. C.S.Raghavendra Krishna, M.Sivalingam and Tarib znati, “Wireless Sensor Networks”,
Springer publication, 2004.4. Holger Karl , Andreas willig, “Protocol and Architecture for Wireless Sensor Networks”, John
wiley publication, Jan 2006.
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5. K.Akkaya and M.Younis, “ A Survey of routing protocols in wireless sensor networks”, ElsevierAdhoc Network Journal, Vol.3, no.3,pp. 325-349, 2005.
6. Philip Levis, “ TinyOS Programming”, 2006 – www.tinyos.net.7. I.F. Akyildiz, W. Su, Sankarasubramaniam, E. Cayirci, “Wireless sensor networks: a survey”,
computer networks, Elsevier, 2002, 394 - 422.8. Jamal N. Al-karaki, Ahmed E. Kamal, “Routing Techniques in Wireless sensor networks: A
survey”, IEEE wireless communication, December 2004, 6 – 28.
DS7005 CRYPTOGRAPHIC TECHNIQUES L T P C3 0 0 3
UNIT I SYMMETRIC CIPHERS 9Overview – classical Encryption Techniques – Block Ciphers and the Data Encryption standard –Introduction to Finite Fields – Advanced Encryption standard – Contemporary Symmetric Ciphers– Confidentiality using Symmetric Encryption.
UNIT II PUBLIC-KEY ENCRYPTION AND HASH FUNCTIONS 9Introduction to Number Theory – Public-Key Cryptography and RSA – Key Management – Diffie-Hellman Key Exchange – Elliptic Curve Cryptography – Message Authentication and HashFunctions – Hash Algorithms – Digital Signatures and Authentication Protocols.
UNIT III NETWORK SECURITY PRACTICE 9Authentication Applications – Kerberos – X.509 Authentication Service – Electronic mail Security –Pretty Good Privacy – S/MIME – IP Security architecture – Authentication Header – EncapsulatingSecurity Payload – Key Management.
UNIT IV SYSTEM SECURITY 9Intruders – Intrusion Detection – Password Management – Malicious Software – Firewalls –Firewall Design Principles – Trusted Systems.
UNIT V WIRELESS SECURITY 9Introduction to Wireless LAN Security Standards – Wireless LAN Security Factors and Issues.
TOTAL : 45 PERIODS
TEXT BOOKS:1. William Stallings, “Cryptography And Network Security – Principles And Practices”, Pearson
Education, 3rd Edition, 2003.
REFERENCES:1. Atul Kahate, “Cryptography and Network Security”, Tata McGraw Hill, 2003.2. Bruce Schneier, “Applied Cryptography”, John Wiley and Sons Inc, 2001.3. Stewart S. Miller, “Wi-Fi Security”, McGraw Hill , 2003.4. Charles B. Pfleeger, Shari Lawrence Pfleeger, “Security In Computing”, 3rd Edition,Pearson
Education, 2003.5. Mao, “Modern Cryptography: Theory and Practice” , First Edition, Pearson Education,
2003.
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DS7006 ADAPTIVE SIGNAL PROCESSING L T P C3 0 0 3
OBJECTIVES: This course is intended to impart to the students the principles of Adaptive signal processing, Different algorithms used for design of Adaptive Filters, Performance evaluation of systems Modeling systems like multipath communication channel Synthesis of filters
UNIT I INTRODUCTION 9Introduction to Adaptive Processing General properties, filtering, prediction and smoothing,applications in Communications: Equalisation, Echo cancellation, Noise cancellation.
UNIT II ORTHOGONALIZED ADAPTIVE FILTERS 9Optimal Signal Processing Principles of orthogonality, minimum square error, Wiener Hopfequations, state space model, innovations process, Kalman filter equations. Linear AdaptiveEqualization Gradient search and steepest descent adaptation algorithms, effect of eigen valuespread on stability and rate of convergence
UNIT III LEAST MEAN SQUARES ADAPTIVE FILTER 9stochastic gradient descent using Least Mean Squares (LMS) algorithms, transient and steadystate properties including convergence rate and mis-adjustment, least square estimation, normalequations, Recursive Least Squares (RLS) algorithms, relationship between RCS and Kalmanfilters.
UNIT IV KALMAN FILTER THEORY 9Kalman Filter theory; Introduction; recursive minimum mean square estimation for scalar randomvariables; statement of the kalman filtering problem: the innovations process; Estimation of stateusing the innovations process
UNIT V FAST RECURSIVE ALGORITHMS AND APPLICATIONS 9Introduction to Fast Recursive Algorithms for Equalisation Adaptive linear prediction, lattice filteringfor RLS. Other Applications Echo cancellation in wowire systems, Noise cancellation.
TOTAL: 45 PERIODSREFERENCES:1. Adaptive Signal Processing, B. Widrow, S. Stearns, Prentice-Hall, 19852. Statistical and Adaptive Signal Processing: Spectral Estimation, Signal Modeling, Adaptive
Filtering and Array Processing, D. Manolakis, V. Ingle, S. Kogan, McGraw Hill, 19993. Adaptive Signal Processing, L. Sibul, Ed., IEEE Press, 19874. Adaptive Filters: Structures, Algorithms and Applications, M. Honig, D. Messerschmitt, Kluwer,
1984.5. Fundamentals of Adaptive Filtering, Ali H. Sayed, John Wiley, 2003.6. Mohinder S. Grewal, Angus P. Andrews, Kalman Filtering: Theory and Practice Using
MATLAB, John Wiley & Sons. 2008.
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DS7007 UNDERWATER ACOUSTICS SIGNAL PROCESSING L T P C3 0 0 3
OBJECTIVES: To understand the characteristics of Underwater Channel To understand the principles of SONAR To understand the challenges in underwater signal processing
OUTCOMES: To be able to design underwater signal processing systems To be able to analyze the performance of underwater signal processing systems
UNIT I UNDERWATER ACOUSTIC CHANNEL 9Underwater Channel Characterization – Sound Transmission Losses-Acoustic Characteristics ofsurface layer-Ambient Noise in the ocean- Correlation properties of Ambient Noise
UNIT II SONAR 9Basics of SONAR- correlation and ambiguities-Wideband Synthetic Aperture SONAR processing-Discrete Spatial arrays-Beam stearing- Target Angle Estimation –Array Shading:
UNIT III TARGET DETECTION 9Passive Acoustic signatures of Ships and Submarines-Target strength for Active Systems-Hypothesis testing- receiver operating Characteristics-estimation of signal Parameters
UNIT IV STATISTICAL PROCESSING & ADAPTIVE SPATIAL FILTERING 9Monostatic Sounding of Single point Targets-Target strength estimation from Echo ensemble-Optimum Filter for Maximum SNR-High Resolution Beam Forming –
UNIT V UNDERWATER ACOUSTIC COMMUNICATION 9Underwater Bio Telemetry System -system Design principle-Speech Coding and Decoding-Transmission and Detection of speech
TOTAL: 45 PERIODSREFERENCES:1.William S. Burdic, Underwater Acoustic Systems, Prentice Hall Inc., 20022.Robert S.H. Istepanian and Milica Stojan, Underwater Acoustic Digital signal processing &
communication system, Kluwer academic Publisher, 2002
DS7008 EMBEDDED SYSTEM DESIGN L T P C3 0 0 3
UNIT I EMBEDDED DESIGN LIFE CYCLE 9Product specification – Hardware / Software partitioning – Detailed hardware and software design– Integration – Product testing – Selection Processes – Microprocessor Vs Micro Controller –Performance tools – Bench marking – RTOS Micro Controller – Performance tools – Benchmarking – RTOS availability – Tool chain availability – Other issues in selection processes.
UNIT II PARTITIONING DECISION 9Hardware / Software duality – coding Hardware – ASIC revolution – Managing the Risk – Co-verification – execution environment – memory organization – System startup – Hardwaremanipulation – memory mapped access – speed and code density.
UNIT III INTERRUPT SERVICE ROUTINES 9Watch dog timers – Flash Memory basic toolset – Host based debugging – Remote debugging –ROM emulators – Logic analyser – Caches – Computer optimization – Statistical profiling
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UNIT IV IN CIRCUIT EMULATORS 9Buller proof run control – Real time trace – Hardware break points – Overlay memory – Timingconstraints – Usage issues – Triggers.
UNIT V TESTING 9Bug tracking – reduction of risks & costs – Performance – Unit testing – Regression testing –Choosing test cases – Functional tests – Coverage tests – Testing embeddedsoftware – Performance testing – Maintenance.
` TOTAL : 45 PERIODSREFERENCES:1. Arnold S. Berger – “Embedded System Design”, CMP books, USA 2002.2. Sriram Iyer, “Embedded Real time System Programming”3. ARKIN, R.C., Behaviour-based Robotics, The MIT Press, 1998.
DS7009 REAL TIME OPERATING SYSTEM DESIGN L T P C3 0 0 3
UNIT I REVIEW OF OPERATING SYSTEMS 9Basic Principles – System Calls – Files – Processes – Design and Implementation of processes –Communication between processes – Operating System structures.
UNIT II DISTRIBUTED OPERATING SYSTEMS 9Topology – Network types – Communication – RPC – Client server model – Distributed file system– Design strategies.
UNIT III REAL TIME MODELS AND LANGUAGES 9Event Based – Process Based and Graph based Models – Petrinet Models – Real TimeLanguages – RTOS Tasks – RT scheduling - Interrupt processing – Synchronization – ControlBlocks – Memory Requirements.
UNIT IV REAL TIME KERNEL 9Principles – Design issues – Polled Loop Systems – RTOS Porting to a Target – Comparison andstudy of various RTOS like QNX – VX works – PSOS – C Executive – Case studies.
UNIT V RTOS APPLICATION DOMAINS 9RTOS for Image Processing – Embedded RTOS for voice over IP – RTOS for fault TolerantApplications – RTOS for Control Systems.
TOTAL: 45 PERIODSREFERENCES:1. Herma K., “Real Time Systems – Design for distributed Embedded Applications”, Kluwer
Academic, 1997.2. Charles Crowley, “Operating Systems-A Design Oriented approach” McGraw Hill 1997.3. C.M. Krishna, Kang, G.Shin, “Real Time Systems”, McGraw Hill, 1997.4. Raymond J.A.Bhur, Donald L.Bailey, “An Introduction to Real Time Systems”, PHI 1999.
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DS7010 MICROCONTROLLER SYSTEM DESIGN AND ANALYSIS L T P C3 0 0 3
UNIT I 8051 ARCHITECTURE 9Basic organization – 8051 CPU structure – Register file – Interrupts – Timers – Port circuits –Instruction set – Timing diagram – Addressing modes – Simple Program and Applications.
UNIT II 8051 PROGRAMMING 9Assembly language programming – Arithmetic Instructions – Logical Instructions –Single bitInstructions – Timer Counter Programming – Serial Communication Programming InterruptProgramming – RTOS for 8051 – RTOSLite – FullRTOS –Task creation and run – LCD digitalclock/thermometer using FullRTOS
UNIT III PIC MICROCONTROLLER 9Architecture – memory organization – addressing modes – instruction set – PIC programming inAssembly & C –I/O port, Data Conversion, RAM & ROM Allocation, Timer programming, MP-LAB.
UNIT IV PERIPHERAL OF PIC MICROCONTROLLER 9Timers – Interrupts, I/O ports- I 2C bus-A/D converter-UART- CCP modules -ADC, DACand Sensor Interfacing –Flash and EEPROM memories.
UNIT V SYSTEM DESIGN – CASE STUDY 9Interfacing LCD Display – Keypad Interfacing - Generation of Gate signals for converters andInverters - Motor Control – Controlling AC appliances –Measurement of frequency – Stand aloneData Acquisition System.
TOTAL : 45 PERIODSREFERENCES:1. John B.Peatman, “Design with Micro controllers”, McGraw Hill international Limited, Singapore,
1989.2. Michael Slater, “Microprocessor based design A comprehensive guide to effective Hardware
design” Prentice Hall, New Jersey, 1989.3. Ayala, Kenneth, “The 8051 Microcontroller” Upper Saddle River, New Jersey Prentice Hall,
2000.4. Muhammad Ali Mazidi, Janice Gillispie mazidi. “The 8051 Microcontroller and Embedded
systems”, Person Education, 2004.5. Muhammad Ali Mazidi, Rolin D. Mckinlay, Danny Causey ‘ PIC Microcontroller and Embedded
Systems using Assembly and C for PIC18’, Pearson Education 20086. John Iovine, ‘PIC Microcontroller Project Book ’, McGraw Hill 20007. Myke Predko, “Programming and customizing the 8051 microcontroller”, Tata McGraw Hill
2001.
DS7011 DIGITAL SIGNAL COMPRESSION L T P C3 0 0 3
UNIT I INTRODUCTION OF COMPRESSION TECHNIQUES 9Compression techniques, Modeling & Coding, Distortion criteria, Differental Entropy, RateDistortion Theory, Vector Spaces, Information theory, Models for sources, Coding – uniquelydecodable codes, Prefix codes, Kraft McMilan Inequality.
UNIT II QUANTIZATION 9Quantization problem, Uniform Quantizer, Adaptive Quantization, Non-uniform Quantization;Entropy coded Quantization, Vector Quantization, LBG algorithm, Tree structured VQ, StructuredVQ, Variations of VQ – Gain shape VQ, Mean removed VQ, Classified VQ, Multisage VQ, AdaptiveVQ, Trellis coded quantization, Differential Encoding, Basic algorithm, Prediction in DPCM,Adaptive DPCM, Delta Modulation, Speech coding – G.726, Image coding.
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UNIT III TRANSFORM CODING 9Transforms – KLT, DCT, DST, DWHT; Quantization and coding of transform coefficients,Application to Image compression – JPEG, Application to audio compression, Sub-Band Coding:Filters, Sub-band coding algorithm, Design of filter banks, Perfect reconstruction using two channelfilter banks, M-band QMF filter banks, Poly-phase decomposition, Bit allocation, Speech coding –G.722, Audio coding – MPEG audio, Image compression.
UNIT IV WAVELET BASED COMPRESSION 9Wavelets, Multiresolution analysis & scaling function, Implementation using filters, Imagecompression – EZW, SPIHT,JPEG 2000. Analysis / Synthesis Schemes: Speech compression –LPV-10, CELP, MELP, Image Compression – Fractal compression.
UNIT V LOSSLESS CODING 9Huffman coding, Adaptive Huffman coding, Golomb codes, Rice codes, Trunstall codes,Applications of Huffman coding, Arithmetic coding, Algorithm, implementation, Applications ofArithmetic coding, Dictionary techniques – LZ77, LZ78, Applications of LZ78-JBIG, JBIG2,Predictive coding – Prediction with partial match, Burrows Wheeler Transform, Applications –CALIC, JPEG-LS, Facsimile coding – T.4, T.6
TOTAL: 45 PERIODSREFERENCES:1. K. Sayood, Introduction to Data Compression, Harcourt India Pvt. Ltd. & Morgan Kaufmann
Publishers, 1996.2. N. Jayant and P. Noll, Digital Coding of Waveforms: Principles and Applications to Speech and
Video, Prentice Hall, USA, 1984.3. D. Salomon, Data Compression: The Complete Reference, Springer, 2000. Z. Li and M.S.
Drew, Fundamentals of Multimedia, Pearson Education (Asia) Pte. Ltd., 2004.4. Ralt Steinmetz and Klara Nahrstedt, Multimedia: Computing, Communication & Applications,
Pearson Education Publications, 2004.5. Rafael C. Gonzaleze & Richard E. Woods, Digital Image Processing Prentice Hall Publications.
MU7011 VIDEO COMPRESSION L T P C3 0 0 3
OBJECTIVES : To introduce principles and current technologies of multimedia systems. To study the issues in effectively representing, processing and transmitting multimedia data
including text, graphics, sound and music, image and video. To study the Image, video and audio standards such as JPEG, MPEG, H.26x, Dolby Digital
and AAC will be reviewed. To study the applications such as video conferencing, multimedia data indexing and retrieval
will also be introduced.
UNIT I INTRODUCTION 9Overview of image compression - important information theory concepts - entropy definition andinterpretation - Shannon-Fanon coding - Huffman coding - Adaptive Huffman coding - Lempel-Zivcodec- QM codec, context-based QM coder - examples of lossless compression
UNIT II QUANTIZATION 9Scalar quantization, optimal scalar quantizer, commander- Vector quantization- Audio and speechcompression- JPEG & JPEG-2000 still image compression- Video coding standards (A) MPEG-1,MPEG-2
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UNIT III VIDEO PROCESSING 9Video coding standards H.264/AVC and HEVC- Video coding techniques - motion estimation, ratecontrol algorithms, pre & post processing- Video delivery/streaming over wired and wirelessnetworks
UNIT IV ADVANCED VIDEO CODING TECHNIQUES 9Mobile multimedia computing- Multimedia content management and protection- Future directions –Multi-view video coding, depth coding and others
UNIT V CONTENT MANAGEMENT 9Video Compression-Motion Compensation, H.261 standard – FMM-14 Multimedia ApplicationsContent-based retrieval in digital libraries – FMM
TOTAL: 45 PERIODSOUTCOMES:Upon Completion of the course, the students will be able
To know principles and current technologies of multimedia systems To know issues in effectively representing, processing, and retrieving multimedia data To know the areas by implementing some components of a multimedia streaming system To know the latest web technologies and some advanced topics in current multimedia
research
REFERENCES:1. Handbook of Image and Video processing - Al Bovik (Alan C Bovik), Academic Press,
Second Edition, 2005.2. Digital Image Sequence Processing, Compression, and Analysis - Todd R. Reed, CRC Press,
2004.3. H.264 and MPEG-4 Video Compression: Video Coding for Next Generation Multimedia -
Iain E.G. Richardson, Wiley, 20034. Digital Video Processing - A. Murat Tekalp, Prentice Hall, 19955. Andy Beach, “Real World Video Compression” Pearson Education, 2010.6. Peter D. Symes ,” Video Compression Demystified” McGraw-Hill, 2001.7. Yun Q. Shi, Huifang Sun,” Image and Video Compression for Multimedia Engineering
Fundamentals, Algorithms, and Standards” 2nd Edition 2008.
MU7102 MULTIMEDIA COMMUNICATION NETWORKS L T P C3 0 0 3
OBJECTIVES:• To understand the Multimedia Communication Models• To analyze the Guaranteed Service Model• To study the Multimedia Transport in Wireless Networks• To solve the Security issues in multimedia networks• To explore real-time multimedia network applications
UNIT I MULTIMEDIA COMMUNICATION MODELS 9Architecture of Internet Multimedia Communication- Protocol Stack-Requirements and Designchallenges of multimedia communications- Multimedia distribution models-Unicasting,Broadcasting and Multicasting.
UNIT II GUARANTEED SERVICE MODEL 9Multicast routing-PIM- Best effort service model and its limitations- QoS and its metrics-Queuingtechniques-WFQ and its variants-RED-QoS aware routing -Call Admission Control-RSVP- Policingand Traffic Shaping algorithms- QoS architectures.
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UNIT III MULTIMEDIA TRANSPORT 9End to end solutions-Multimedia over TCP-Significance of UDP- Multimedia Streaming- Audio andVideo Streaming-Interactive and non Interactive Multimedia- RTSP- RTP/RTCP – SIP-H.263.
UNIT IV MULTIMEDIA OVER WIRELESS NETWORKS 9End to end QoS Provisioning-QoS enhancements-Call Admission Control-QoS Management-Multimedia support in 3G & 4G networks- Location Based Multimedia Service System.
UNIT V MULTIMEDIA NETWORK SECURITY AND APPLICATIONS 9Security threats in Multimedia Communication- Digital Rights Management Architecture-DRM forMobile Multimedia- Architectures, Requirements and Design Challenges of real time MultimediaNetwork Applications- Case Study-VoIP- Video Conferencing- Military Surveillance- Interactive TV-Video on Demand- Smart Phone.
TOTAL 45 PERIODSOUTCOMES:At the end of the course, the students will be able to
• deploy the right multimedia communication models• apply QoS to multimedia network applications with efficient routing techniques• solve the security threats in the multimedia networks• develop the real-time multimedia network applications
REFERENCES:1. K. R. Rao, Zoran S. Bojkovic, Dragorad A. Milovanovic, “Introduction to Multimedia
Communications Applications, Middleware, Networking”, John Wiley and Sons, 2006.2. Jean Warland, Pravin Vareya, “High Performance Networks”, Morgan Kauffman Publishers,
2002.3. William Stallings, “High Speed Networks and Internets Performance and Quality of Service”, 2nd
Edition, Pearson Education, 2002.4. Aura Ganz, Zvi Ganz, Kitti Wongthawaravat, ‘Multimedia Wireless Networks Technologies,
Standards and QoS’, Prentice Hall, 2003.5. Mahbub Hassan and Raj Jain, “High Performance TCP/IP Networking”, Pearson
Education, 20046. Shiguo Lian, “Multimedia Communication Security Recent Advances”, Nova Science
Publishers, 2008.
CP7204 ADVANCED OPERATING SYSTEMS L T P C3 0 0 3
OBJECTIVES: To learn the fundamentals of Operating Systems To gain knowledge on Distributed operating system concepts that includes architecture, Mutual
exclusion algorithms, Deadlock detection algorithms and agreement protocols To gain insight on to the distributed resource management components viz. the algorithms for
implementation of distributed shared memory, recovery and commit protocols To know the components and management aspects of Real time, Mobile operating systems
UNIT I FUNDAMENTALS OF OPERATING SYSTEMS 9Overview – Synchronization Mechanisms – Processes and Threads - Process Scheduling –Deadlocks: Detection, Prevention and Recovery – Models of Resources – Memory ManagementTechniques.
UNIT II DISTRIBUTED OPERATING SYSTEMS 9Issues in Distributed Operating System – Architecture – Communication Primitives – Lamport’sLogical clocks – Causal Ordering of Messages – Distributed Mutual Exclusion Algorithms –Centralized and Distributed Deadlock Detection Algorithms – Agreement Protocols.
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UNIT III DISTRIBUTED RESOURCE MANAGEMENT 9Distributed File Systems – Design Issues - Distributed Shared Memory – Algorithms forImplementing Distributed Shared memory–Issues in Load Distributing – Scheduling Algorithms –Synchronous and Asynchronous Check Pointing and Recovery – Fault Tolerance – Two-PhaseCommit Protocol – Nonblocking Commit Protocol – Security and Protection.
UNIT IV REAL TIME AND MOBILE OPERATING SYSTEMS 9Basic Model of Real Time Systems - Characteristics- Applications of Real Time Systems – RealTime Task Scheduling - Handling Resource Sharing - Mobile Operating Systems –Micro KernelDesign - Client Server Resource Access – Processes and Threads - Memory Management - Filesystem.
UNIT V CASE STUDIES 9Linux System: Design Principles - Kernel Modules - Process Management Scheduling - MemoryManagement - Input-Output Management - File System - Interprocess Communication. iOS andAndroid: Architecture and SDK Framework - Media Layer - Services Layer - Core OS Layer - FileSystem.
OUTCOMES:Upon Completion of the course, the students should be able to:1. Discuss the various synchronization, scheduling and memory management issues2. Demonstrate the Mutual exclusion, Deadlock detection and agreement protocols of Distributed
operating system3. Discuss the various resource management techniques for distributed systems4. Identify the different features of real time and mobile operating systems5. Install and use available open source kernel6. Modify existing open source kernels in terms of functionality or features used
REFERENCES:1. Mukesh Singhal and Niranjan G. Shivaratri, “Advanced Concepts in Operating Systems –
Distributed, Database, and Multiprocessor Operating Systems”, Tata McGraw-Hill, 2001.2. Abraham Silberschatz; Peter Baer Galvin; Greg Gagne, “Operating System Concepts”,
Seventh Edition, John Wiley & Sons, 2004.3. Daniel P Bovet and Marco Cesati, “Understanding the Linux kernel”, 3rd edition, O’Reilly,
2005.4. Rajib Mall, “Real-Time Systems: Theory and Practice”, Pearson Education India, 2006.5. Neil Smyth, “iPhone iOS 4 Development Essentials – Xcode”, Fourth Edition, Payload media,
2011.
DS7012 DESIGN AND ANALYSIS OF ALGORITHMS L T P C3 1 0 4
UNIT I INTRODUCTION 10Fundamentals of algorithmic problem solving – Important problem types – Fundamentals of theanalysis of algorithm efficiency – analysis frame work – Asymptotic notations – Mathematicalanalysis for recursive and non-recursive algorithms.
UNIT II DIVIDE AND CONQUER METHOD AND GREEDY METHOD 12Divide and conquer methodology – Merge sort – Quick sort – Binary search – Binary treetraversal – Multiplication of large integers – Strassen’s matrix multiplication – Greedy method –Prim’s algorithm – Kruskal’s algorithm – Dijkstra’s algorithm.
UNIT III DYNAMIC PROGRAMMING 12Computing a binomial coefficient – Warshall’s and Floyd’ algorithm – Optimal binary search tree –Knapsack problem – Memory functions.
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UNIT IV BACKTRACKING AND BRANCH AND BOUND 14Backtracking – N-Queens problem – Hamiltonian circuit problem – Subset sum problem – Branchand bound – Assignment problem – Knapsack problem – Traveling salesman problem.
UNIT V NP-HARD AND NP-COMPLETE PROBLEMS 12P & NP problems – NP-complete problems – Approximation algorithms for NP-hard problems –Traveling salesman problem – Knapsack problem.
L : 45 T : 15 TOTAL : 60 PERIODSREFERENCES:1. Anany Levitin “Introduction to the Design and Analysis of Algorithms” Pearson Education 2003.2. Thomas H.Cormen, Charles E.Leiserson, Ronald L.Rivest, “Introduction to algorithms” Prentice
Hall 1990.
DS7013 DIGITAL MODULATION AND CODING L T P C3 0 0 3
UNIT I BAND PASS DATA TRANSMISSION 9Geometric representation of signals; Coherent detection of signals in noise. Correlation receiverand matched filter receiver.
UNIT II DIGITAL MODULATION 9Probability of error and receiver implementation of BPSK, BFSK, QPSK, MSK, Mary PSK and MaryFSK.
UNIT III DEMODULATION OF SIGNALS 9Union bound approximation for the probability of error. Detection of signals with unknown phaseSpectra of digitally modulated signals. Carrier and clock recovery methods.
UNIT IV ERROR CONTROL CODES 9Error control coding: Linear block codes: Generator and parity check matrices. Syndromecalculation. Error detection and error correction using block codes. Conventional codes:Generator and transfer function matrices.
UNIT V CONVENTIONAL CODES 9State and trellis diagrams. Maximum likelihood decoding of conventional codes: The Viterbialgorithm. Trellis coded modulation.
TOTAL: 45 PERIODSREFERENCES:1. Stephen G. Wilson, Digital Modulation and Coding, Prentice Hall, 1996.2. M. K. Simon S. M. Winedi and N. C. Lindsey “ DIGITAL COMMUNICATION TECHNIQUES’’
Signalling and detection preatice wall India, New Delhi 1953. Simon Waykin Digital Communication Johnwiley and sons 1998.4. John G. Prakis “Digital Communication 4th Edition McGRAW HILL Publication 2012..5. Theodooc S. Rappaport “ Wireless Communication 2nd Edition “Pearson Education 2002.
